非线性系统多故障诊断方法

宋华; 张洪钺; 王行仁

非线性系统多故障诊断方法

北京航空航天大学自动化科学与电气工程学院, 北京 100083

基金项目: 国家自然科学基金资助项目(60234010,60572185)

详细信息

中图分类号: TP 277
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- 被引次数: 0
出版历程
- 收稿日期: 2004-08-23
- 网络出版日期: 2005-11-30

Multiple faults diagnosis approach for nonlinear system

School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China

摘要

摘要: 给出了非线性系统的一种基于模糊奇偶方程的多故障诊断方法.解决了非线性系统中同时出现多种故障时的故障检测与识别问题.首先构造线性系统的全解耦奇偶方程,再应用T-S模型融合非线性系统各个工作点处的线性模型的全解耦奇偶方程得到模糊奇偶方程.模糊奇偶方程产生的残差仅对一个执行器故障敏感、对一个传感器不敏感,而对其他执行器不敏感、对其他传感器敏感.将传感器和执行器故障模型表示成偏差的形式,根据残差信息可以估计出故障的模型参数.给出了应用递推最小二乘方法对各故障模型的参数进行估计的方法.给出了铁路牵引控制系统的感应电机仿真实例.结果表明,新方法能够对传感器故障和执行器故障同时存在的多故障进行诊断.
- 故障诊断 /
- 模糊集 /
- 最小二乘 /
- 非线性系统 /
- 多故障 /
- 奇偶方程
Abstract: A new method for multiple faults diagnosis of nonlinear systems based on fuzzy parity equation was presented. This approach can be used to detect and identify the fault models when there are several kinds fault such as actuator fault and sensor fault simultaneously in the system. Fully decoupled parity equation for multiple faults was constructed, and fuzzy parity equation was obtained by using the T-S fuzzy model. Residual generated by the fuzzy parity equation is sensitive to one special actuator fault and all sensor faults except one special sensor, and insensitive to other actuators and the special sensor. The faults in sensors and actuators were represented as biases. The fault parameter can be identified from the information contained in the residuals. A parameter estimator based on recursive least square was designed. A simulation example on an induction motor of a railway traction system was given for illustration. Results show that the new approach can be used to detect and identify the faults of multiple sensors and multiple actuators in nonlinear systems.
- fault diagnosis /
- fuzzy sets /
- least square approximations /
- nonlinear systems /
- multiple faults /
- parity equation

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[1] Chow E Y, Willsky A S. Analytical redundancy and the design of robust failure detection systems[J]. IEEE Trans Automatic Control, 1984, AC-29(7):603~614 [2] Lou X C, Willsky A S, Verghese G C. Optimally robust redundancy relations for failure detection in uncertain systems[J]. Automatica, 1986, 22(3):333~344 [3] Zhang H Y, Patton R J. Optimal design of robust analytical redundancy for uncertain systems. In:IEEE Region 10 Conference on Computer, Communication, Control and Power Engineering. Beijing, 1993 [4] 金宏,张洪钺,金忠. 对特定传感器故障敏感的最优奇偶向量检测与隔离方法[J]. 航空学报,1997,18(4):481~483 Jin Hong, Zhang Hongyue, Jin Zhong. Fault detection and isolation using optimal parity vector sensitive to special sensor fault [J]. ACTA Aeronautica et Astronautica Sinica, 1997, 18(4):481~483(in Chinese) [5] 金宏,张洪钺. 动态系统慢变型故障的检测与隔离[J]. 航天控制,2000(4):65~70 Jin Hong, Zhang Hongyue. FDI of slow-grown faults of dynamic systems [J]. Aerospace Control, 2000(4):65~70 (in Chinese) [6] 宋华,张洪钺. 基于全解耦奇偶方程的动态系统执行器故障检测与识别. 航天控制, 2002(1):65~70 Song Hua, Zhang Hongyue. A fully-decoupled parity equation approach to actuator fault detection and identification of dynamic systems [J]. Aerospace Control, 2002(1):65~70(in Chinese) [7] Song Hua, Zhang Hongyue. An aapproach to sensor fault diagnosis based on fully-decoupled parity equation and parameter estimate. In:International Conference of the 4th World Congress on Intelligent Control and Automation. Shanghai:Press of East China University of Science and Technology, 2002.2750~2754 [8] Garcia F J, Izquierdo V, de Miguel L J, et al. Fault-diagnostic system using analytical fuzzy redundancy[J]. Engineering Applications of Artificial Intelligence,2000(13):441~450 [9] Schram G, Gopisetty S, Stengel R F. A fuzzy logic-parity space approach to actuator failure detection and identification. AIAA 98-1014,1998 [10] Gopisetty Sai M, Stengel R F. Detecting and identifying multiple failures in a flight control system. AIAA-98-4488, 1998 [11] 宋华,张洪钺. 多故障的奇偶方程-参数估计诊断方法[J]. 控制与决策,2003,18(4):413~417 Song Hua, Zhang Hongyue. An approach for multiple faults diagnosis based on parity equation and parameter estimation[J]. Control and Decision, 2003,18(4):413~417(in Chinese) [12] Patton R J, Chen J, Lopez-Toribio C J. Fuzzy observers for non-linear dynamic systems fault diagnosis. In:Proceedings of the 37th IEEE Conference on Decision & Control. Tampa, 1998.84~89 [13] Wang L X. Adaptive fuzzy systems and control:design stability aanalysis[M]. Englewood:PTR Prentice-Hall, 1994.1~10 [14] Leung Chi Sing, Wong Kwok Wo, Sum Pui Fai, et al. On-line training and pruning for recursive least square algorithms[J]. IEE Electronics Letters, 1996, 32(23):2152~2153

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